Abstract:

A group of solar power stations with inverters are adjusted in order to
achieve optimum power output in accordance with maximum power-point
tracking (MPPT). The MPPT data is used to perform adjustments. Power
measurement factors, including Maximum Power Points (MPPs) are
established to represent a bus-voltage setting that produces the maximum
power output from an individual photovoltaic panel. These settings are
established for the group so as to optimize power output under a variety
of operating conditions.

Claims:

1. A method of controlling a power network of multiple power generating
stations, the method comprising:connecting a plurality of power-producing
elements to a communications network;setting each power-producing element
to operate as either a master or a slave within the network.

2. The method of claim 1, further comprising setting the operating point
of one or more inverters associated with the power generating stations
based on maximum power points (MPPs) established elsewhere in the
network.

3. The method of claim 2, further comprising dynamically selecting one of
said inverters to function as the master in accordance with predetermined
criteria to select, as the master, an inverter which provides a
representative sample.

4. The method of claim 3, further comprising:the inverters mapped
according to said predetermined criteria;in the case of the mapping
resolving to a mapped cluster of said inverters that spans more than one
allowable error margin, reducing the error margin to more clearly isolate
the mapped clusters.

5. The method of claim 1, further comprising:the master performing maximum
power-point tracking (MPPT); andthe setting an operating point of at
least one slave to that of the master.

6. The method of claim 1, further comprising:comparing power measurement
factors from at least a subset of power stations in the power network to
generate power measurement factor comparison data;performing maximum
power-point tracking (MPPT) based on the power measurement factor
comparison data to provide MPPT data; andperforming MPPT adjustments
based on the MPPT data.

7. The method of claim 6, further comprising:using photovoltaic panels as
at least a subset of the power generating stations;using maximum power
points (MPPs) as the power measurement factors, wherein the MPP
represents a bus-voltage setting that produces the maximum power output
from an individual photovoltaic panel; andthe MPPT provides adjustments
for variation in MPP factors.

8. The method of claim 1, further comprising:comparing power measurement
factors from at least a subset of power stations in the power network to
generate power measurement factor comparison data;performing maximum
power-point tracking (MPPT) based on the power measurement factor
comparison data to provide MPPT data;dynamically selecting one of said
power stations to function as the master in accordance with predetermined
criteria to select, as the master, a power station which provides a
representative sample; andperforming MPPT adjustments based on the MPPT
data.

9. The method of claim 8, further comprising:using photovoltaic panels as
at least a subset of the power generating stations;using maximum power
points (MPPs) as the power measurement factors, wherein the MPP
represents an operating point that produces the maximum power output from
an individual photovoltaic panel; andthe MPPT provides adjustments for
variation in at least one MPP factor.

10. The method of claim 8, further comprising:using photovoltaic panels as
at least a subset of the power generating stations;using maximum power
points (MPPs) as the power measurement factors, wherein the MPP
represents an operating point that produces the maximum power output from
an individual photovoltaic panel; andthe MPPT provides adjustments for
variation in irradiance.

11. The method of claim 1, further comprising:comparing power measurement
factors from at least a subset of power stations in the power network to
generate power measurement factor comparison data;using photovoltaic
panels as at least a subset of the power generating stations;using
maximum power points (MPPs) as the power measurement factors, wherein the
MPP represents a bus-voltage setting that produces the maximum power
output from an individual photovoltaic panel; andstoring at least one MPP
factor;retrieving the stored MPP factor;measuring at least one real-time
MPP factor;using the stored MPP factor and the real-time MPP factor to
perform maximum power-point tracking (MPPT).

12. The method of claim 1, further comprising:comparing power measurement
factors from at least a subset of power stations in the power network to
generate power measurement factor comparison data;using photovoltaic
panels as at least a subset of the power generating stations;using
maximum power points (MPPs) as the power measurement factors, wherein the
MPP represents a bus-voltage setting that produces the maximum power
output from an individual photovoltaic panel; andstoring at least one MPP
factor;retrieving the stored MPP factor;measuring at least one real-time
MPP factor;using the stored MPP factor and the real-time MPP factor to
perform maximum power-point tracking (MPPT) to provide adjustments for
variation in MPP factors over time.

13. The method of claim 1, further comprising:setting the operating point
of one or more inverters associated with the power generating stations
based on maximum power points (MPPs);mapping the inverters in MPP-factor
space;selecting an optimal grouping of masters and slaves according to
predetermined criteria; andre-evaluating and, if appropriate, reassigning
masters and slaves either at fixed intervals or when measured MPP factors
change by more than a threshold amount.

14. The method of claim 1, further comprising:setting the operating point
of one or more inverters associated with the power generating stations
based on maximum power points (MPPs);mapping the inverters in MPP-factor
space;selecting an optimal grouping of masters and slaves according to
predetermined criteria, including a minimum number of masters;
andre-evaluating and, if appropriate, reassigning masters and slaves
either at fixed intervals or when measured MPP factors change by more
than a threshold amount.

15. The method of claim 14, further comprising in the case of the mapping
resolving to a mapped cluster of said inverters that spans more than one
allowable error margin, reducing the error margin to more clearly isolate
the mapped clusters.

16. The method of claim 1, further comprising:comparing power measurement
factors from at least a subset of power stations in the power network to
generate power measurement factor comparison data;using maximum power
points (MPPs) as the power measurement factors, wherein the MPP
represents an operating-point setting that produces the maximum power
output from an individual power generating station.

17. The method of claim 16, further comprising:performing maximum
power-point tracking (MPPT) on inverter outputs of the power stations;
andcausing at least one inverter to operate in response to a master
inverter MPPT measurement, and to use MPP factors within a predetermined
variance from said another inverter in order to select a master inverter.

18. The method of claim 1, further comprising:performing maximum
power-point tracking (MPPT) on inverter outputs of the power stations;
andusing a central control unit to control the performing of the MPPT
adjustments.

19. The method of claim 1, further comprising:performing maximum
power-point tracking (MPPT) on inverter outputs of the power
stations;using a central control unit to control the performing of the
MPPT adjustments;monitoring power produced by at least one slave power
element, and in the event that at least one power station varies from a
maximum power point (MPP) factor, causing that power station to override
the control from the central control unit.

20. The method of claim 1, further comprising:performing maximum
power-point tracking (MPPT) on inverter outputs of the power
stations;using peer-to-peer control to control the performing of the MPPT
adjustments.

21. The method of claim 1, further comprising:performing maximum
power-point tracking (MPPT) on inverter outputs of the power
stations;using predictions of MPPT to control performing of the MPPT
adjustments.

22. The method of claim 1, further comprising:establishing a tracking of
at least one power measurement factor for at least one power generating
station;setting an operating point for the power generating
station;monitoring at least one sensor associated with the station, the
sensor detecting at least one of power, irradiance, and temperature;in
the event of a previous reading, determining whether readings from the
sensor represent a change form the previous reading; andin the case of
the reading changing from the previous reading, effecting an adjustment
in the operating point.

23. A method of controlling a power generating station within a power
network of multiple power generating stations, the method
comprising:establishing a tracking of at least one power measurement
factor for the power generating station;identifying a corresponding power
measurement factor for at least one neighbor power generating
station;using the power measurement factors of the power generating
station and the neighbor power generating station to compare the power
generating station with the neighbor power generating station;using the
comparison to determine operation of the power generating station as one
of a master, controlling the neighbor power generating station, as a
slave controlled by the neighbor power generating station, or as having
no direct control relationship with the neighbor power generating
station;setting an operating point for the power generating station
according to the master or slave relationship of the power generating
station and the neighbor generating station.

24. The method of claim 23, further comprising:monitoring at least one
sensor associated with the station, the sensor detecting at least one of
power, irradiance, and temperature;in the event of a previous reading,
determining whether readings from the sensor represent a change form the
previous reading; andin the case of the reading changing from the
previous reading, effecting an adjustment in the operating point.

25. The method of claim 23, further comprising:setting the operating point
of one or more inverters associated with the power generating stations
based on maximum power points (MPPs); andre-evaluating and, if
appropriate, reassigning masters and slaves either at fixed intervals or
when measured MPP factors change by more than a threshold amount.

26. The method of claim 25, further comprising:setting the operating point
of one or more inverters associated with the power generating stations
based on maximum power points (MPPs);mapping the inverters in MPP-factor
space;selecting an optimal grouping of masters and slaves according to
predetermined criteria, including a minimum number of masters;
andre-evaluating and, if appropriate, reassigning masters and slaves
either at fixed intervals or when measured MPP factors change by more
than a threshold amount.

27. The method of claim 26, further comprising in the case of the mapping
resolving to a mapped cluster of said inverters spans more than one
allowable error margin, reducing the error margin to more clearly isolate
the mapped clusters.

28. A method of controlling a power generating station within a power
network of multiple power generating stations, the method
comprising:performing a selection of power measurement factors;taking a
reading of the selected power measurement factors;determining whether the
power measurement factors represent a change from a last reading
exceeding an allowed margin;in the case of the reading representing
either the change exceeding the allowed margin threshold or the reading
representing an initial reading, searching for another power generating
station with similar power measurement factors;in the case of locating
another power station with similar power measurement factors,
establishing one of the power generating stations as a slave; andin the
case of not locating another power station with similar power measurement
factors, establishing the power generating station as a master.

29. The method of claim 28, further comprising using inverter maximum
power point (MPP) readings as the power measurement factors.

30. A processor comprising circuitry for performing the method of claim
28, comprising said processor provided as a chipset consisting of at
least one monolithic integrated circuit chip.

31. Control apparatus for a power network of multiple power generating
stations, comprising:a circuit module capable of comparing power
measurement factors from at least a subset of power stations in the power
network to generate power measurement factor comparison data;a circuit
module capable of performing maximum power-point tracking (MPPT) based on
the power measurement factor comparison data to provide MPPT data; anda
circuit module capable of performing MPPT adjustments based on the MPPT
data.

32. Control apparatus for a power network of multiple power generating
stations, comprising:means for comparing power measurement factors from
at least a subset of power stations in the power network to generate
power measurement factor comparison data;means for performing maximum
power-point tracking (MPPT) based on the power measurement factor
comparison data to provide MPPT data; andadjustment means for performing
MPPT adjustments based on the MPPT data.

33. The control apparatus of claim 32, further comprising:means for
monitoring at least one power measurement factor;means for determining
whether the monitored power measurement factor results in a variation
from the margin threshold; andmeans for causing that power station to
override the control from the central control unit responsive to a
determination that the monitored power measurement factor results in the
variation from the margin threshold.

34. A computer program product, comprising:a computer-readable medium
comprising:a first instruction for causing a computer to compare power
measurement factors from at least a subset of power stations in the power
network to generate power measurement factor comparison data;a second
instruction for causing the computer to perform maximum power-point
tracking (MPPT) based on the power measurement factor comparison data to
provide MPPT data; anda third instruction for causing the computer to
perform MPPT adjustments based on the MPPT data.

35. The computer program produce of claim 34, further comprising a fourth
instruction for causing the computer to re-evaluate and, if appropriate,
reassign masters and slaves either at fixed intervals or when measured
maximum power point (MPP)factors change by more than a threshold amount.

Description:

BACKGROUND

[0001]1. Field

[0002]This disclosure relates to power control for a network of power
stations. In a particular configuration, inverter power settings are
performed for multiple solar panel stations.

[0003]2. Background

[0004]Solar photovoltaic systems produce electrical power. Electrical
power is the product of current and voltage (I×V). Operating point
and output power are interdependent in individual solar cells, and by
extension in multi-cell panels and multi-panel arrays. The
interdependence is characterized by a set of "I-V curves" as shown in
FIG. 1. Each I-V curve has a "Maximum Power Point" (MPP). This point is
the operating point (voltage and current) at which the product of the
panel's voltage and current provides the highest possible power output
for a given set of environmental conditions (the peaks of the curves on
the lower graph of FIG. 1). In viewing FIG. 1, MPPhigh is the point
on the voltage axis at which the power is maximum for the upper curve;
MPPmedium is the point on the voltage axis at which the power is
maximum for the middle curve; and MPPlow, is the point on the
voltage axis at which the power is maximum for the lower curve. These are
illustrative graphs, but a typical value for the MPPhigh curves
would be 1000 W/m2 and a typical value for the MPPlow curves
would be 200 W/m2. Ideally, each array of photovoltaic cells will be
operating at its MPP to maximize the energy the photovoltaic system can
capture. This ideal can be difficult to achieve because the I-V curve and
MPP of a cell in the field is not constant.

[0005]A number of factors ("MPP factors") influence the MPP of a given
cell, module, panel, or array. They include irradiance (solar radiation
energy received on a given surface area in a given time), cell
temperature, spectral quality, ambient temperature, age of the panel(s),
zenith and azimuth position of the sun, soiling, and wind speed. FIG. 2
is an illustrative example of MPP dependence on temperature for a fixed
irradiance. FIG. 3 is an illustrative example of I-V and power curves for
uniform and non-uniform irradiance. The examples are given for
explanation and do not depict actual test results of a particular panel.

[0006]Referring to FIG. 3, I-V curves 311 and 313 correspond to uniform
and non-uniform irradiance, respectively. Power curves 321 and 323
correspond to uniform and non-uniform irradiance, respectively. MPP
voltage for uniform irradiation is indicated at 331. Under circumstances
of non-uniform irradiance, it is possible to have a MPP voltage at a
reduced voltage and it is possible to have local MPP≠global MPP,
indicated at 333.

[0007]In large scale PV systems, on the order of 100's of kilowatts to
10's or 100's of megawatts, a large number of panels or arrays of panels
are used covering large ground surface areas. In these large systems,
temperature-dependent losses in system components, such as wiring and
transformers, also affect the MPP of the system.

[0008]Most of these factors are affected by local weather patterns, which
are unpredictable and can change rapidly.

[0009]FIG. 4 is a diagram of a large solar installation with varying MPP
factors for different arrays and array groups. A complication when
planning large installations is that a large installation may cover
variable terrain that includes hillsides, gullies, bodies of water,
stands of trees, utility easements, or man-made structures. Each of these
factors can affect the external MPP factors acting on nearby panels and
make them behave differently from the reference. With reference to FIG.
4, array Group A's location is "ideal"--a regular grid on flat,
featureless land. Array Group B may get some shade from the hill for part
of the day. Array Group C is on the hill. Array Group D may be affected
by the trees (transient partial shade) or the stream and lake (reflected
irradiance).

[0010]Localized differences in wind speed due to different ground levels
or obstructions will affect ambient and cell temperature. Thus, landscape
features can cause different panels or arrays to experience differing MPP
factors at any given time.

[0011]Even if the terrain is perfectly featureless, as in some plains
regions, broken or moving cloud patterns can affect the MPP of the PV
panels below. The more area the installation covers, the more
opportunities for shifting cloud patterns or fog patches to decrease the
representative accuracy of a reference. Therefore, a need exists for a
scheme to operate as close as possible to the MPP tailored to the needs
of large installations.

[0012]Because PV systems of the past have been relatively small, 100's of
watts to 100's of kilowatts, it has been customary to attempt to keep
each module, panel, or sub-array within the system independently
operating at its MPP. This function, and the systems and methods that
perform it, are collectively known as "Maximum Power Point Tracking"
(MPPT). The MPPT function typically resides in the inverters that receive
DC power produced by the PV panels and convert it to AC power. MPPT
methods may be classified as predictive (based on forecasts of likely
MPP) or reactive (based on real-time feedback of actual system
performance). In either case, each inverter is responsible for handling
the MPPT function for the PV array it is serving.

[0013]Predictive MPPT approaches set the operating point of the PV array
based on a predetermined constant value (selected to represent the
average MPP) or based on an algorithm that adjusts the operating point
based on inputs such as time of day, actual or predicted irradiance
levels, or actual or predicted cell temperature. The disadvantage of
predictive MPPT is that weather-related predictions may be wrong, and the
power output will be sub-optimal if unexpected weather occurs.

[0014]Reactive MPPT methods use real-time measurements of changes in
power, MPP factors, or both as feedback for closed-loop control of array
operating points. These allow arrays to adapt to unexpected conditions.
Reactive MPPT methods include algorithms where the operating point of the
array is periodically varied until the MPP is determined. The
disadvantage of reactive MPPT is that the array's power output is
suboptimal for considerable periods of time while the operating point is
being adjusted. The disadvantage can be compounded when rapid irradiance
changes, as from fast-moving broken clouds, prolong hunt time; the MPP is
a moving target while the I-V curve is changing with irradiance. The
disadvantage can also be aggravated for partially-shaded arrays with
"lumpy" I-V curves having multiple local maxima, an example of which is
depicted in FIG. 3; the system may settle on a local MPP that is not the
global MPP. Finally under quickly changing irradiance conditions, MPPTs
often force the array to operate on the unstable portion of the I-V
curve, which is the region beyond the peak operating point where power
can drop off very quickly and the closed loop tracking system can become
unstable.

[0015]"Reference" reactive MPPT methods track the MPP of a representative
sample, rather than on each module, panel, array, or other independently
controllable unit. The operating points of the other modules, panels, or
arrays are then set to the sample's MPP. The disadvantage is that the
representative sample is never completely representative due to the
sample's size and differences in the MPP factors between the sample and
the actual PV array. Reference MPPT schemes tend to mitigate the
fluctuation problems; the larger the array, the less the reference cell's
MPPT operations affect total output power. In applying this technique,
the larger the number of panels in an installation, the greater the
chance for error due to variability in the cell, panel or array
manufacturing process. Increased geographical coverage of an installation
results in increased variation in external MPP factors that the PV panels
may experience. Both of these factors may compromise the accuracy of
reference cells in tracking MPP for large arrays.

SUMMARY

[0016]Large-scale PV systems present opportunities for each
inverter-connected array in the system to operate at or near its MPP
using information from other arrays in the system. In a multi-unit,
networked system of PV inverters, this approach sets the operating point
of one or more inverters based on an MPP established elsewhere in the
network. The "operating point" may be adjusted by adjusting voltage,
current, or both. Unlike the reference MPPT methods of the prior art, it
is possible to use a reference that is not a permanently fixed separate
cell or sub-array, but an inverter-controlled array that may be selected
dynamically with changing external conditions so that it always
represents, with acceptable accuracy, the most representative sample. In
large systems, there may be multiple representative references
(hereinafter, a "master") each used to set the operating point of other
inverter-controlled arrays. Compared to the prior-art approach of each
inverter-controlled array performing independent MPPT, the technique is
able to increase plant energy capture and reduces fluctuations in the
delivered power. Compared to fixed-reference MPPT, this ensures that the
reference is optimally chosen for the prevailing external conditions.

DESCRIPTION OF THE DRAWINGS

[0017]The features and nature of the present disclosure will become more
apparent from the description set forth below and the drawings, in which
like reference characters identify correspondingly throughout and
wherein:

[0018]FIG. 1 is a graphical representation of operating point verses power
and current in a PV array for high and low irradiance circumstances.

[0019]FIG. 2 is a graphical representation of operating point at a fixed
irradiance for varying cell temperatures

[0020]FIG. 3 is a graphical representation of operating point verses power
and current in a non-uniform, shading example of irradiance, showing
multiple peak power points

[0021]FIG. 4 is a diagram of a large solar installation with varying MPP
factors for different arrays and array groups.

[0022]FIG. 5A is a diagram of an example of a two-dimensional MPP space
with inverters mapped and masters and slaves assigned according to a
predetermined error margin.

[0023]FIG. 5B is a diagram of an example of a two-dimensional MPP-factor
space, with inverters mapped and error margins plotted, showing an
error-proofing algorithm in action to prevent inverters from
inappropriately becoming slaves of other slaves.

[0024]FIGS. 6A-F are flow diagrams showing the control operation for sets
of panels.

[0025]FIG. 6A (prior art) is a diagram of a representative building block
for a large scale PV system.

[0026]FIG. 6B (prior art) is a diagram of a large PV system typical of
conventional installations where MPPT functions are performed at each
inverter.

[0027]FIG. 6c (prior art) is a diagram of representative building blocks
for a large scale PV system capable of network communications.

[0028]FIG. 6D is a diagram of a large PV system with the addition of a
data network that connects each inverter to each other and indicating
that some inverters perform their own MPPT function while other inverters
operate at the MPP point of another inverter. In this embodiment, the
master/slave relationships can be assigned by any node on the network or
by each inverter individually.

[0029]FIG. 6E is a diagram of a large PV system with the addition of a CCU
(central control unit) added to the data network. In this embodiment, the
CCU can make master/slave associations based on data from inverters.

[0030]FIG. 6F is a diagram of a large PV system where the CCU is making
MPP decisions for each inverter.

[0031]FIG. 7 is a diagram indicating a typical flow diagram for a
centrally controlled embodiment evaluating MPP factors and making
master/slave assignments.

[0032]FIG. 8 is a diagram indicating a typical flow diagram for a
self-directed embodiment where each inverter is evaluating MPP factors
and making its own master/slave determination.

DETAILED DESCRIPTION

[0033]Overview

[0034]In a multi-inverter system, groups of arrays will often share
similar internal and external factors that affect their MPP. The
inverters are connected to a common communication network. Each inverter
is capable of operating as a "master" that performs maximum power-point
tracking (MPPT) for its own array, or as a "slave" that sets its
operating point to match another inverter in the network. If only the
master inverters need to perform MPPT when MPP factors change, while the
slaves simply adjust their operating point to match their assigned
masters, the total energy capture of the system will increase.

[0035]While a "common communication network" is described, it is
understood that multiple networks within the meaning of the Open Systems
Interconnection Basic Reference Model (OSI Model) at the OSI Network
Layer and Transport Layer can be used. When connected through a device,
such multiple networks can constitute a single "network" because control
or communication is initiated at one device and received at another
device. By way of example, a "common communication network" can include
groups of inverters connected to separate networks that connect with a
common CCU or networked group of CCUs.

[0036]In larger scale PV systems, on the order of 100's of kilowatts to
10's or 100's of megawatts, a large number of panels or arrays of panels
are used covering very large ground surface areas. In some cases this
could be on the order of 80,000 acres, by way of example. In these large
systems, temperature-dependent losses in system components, such as
wiring and transformers, also affect the MPP of the system. In these
types of systems there are MPPT opportunities that cause the larger scale
system to operate at or near its MPP using information from other PV
systems in the local area. These large scale MPPT systems that rely on
communication from adjacent PV systems have the ability to capture more
energy than a large number of independently running MPPTs that operate
independently from each other.

[0037]The control and peak power tracking of large scale PV plants using
multiple arrays and inverters is accomplished by looking at the power
levels of identified master arrays. Communication between the various
arrays then allows for other arrays within the PV plant to track the
master array.

[0038]Control of a power network having multiple power generating stations
is achieved by use of maximum power-point (MPP) data or other
power-related data. Power measurement factors from at least a subset of
power stations in the power network are compared, and the data and
comparison of the data is used to generate power measurement factor
comparison data. The power measurement factor comparison data is used in
tracking the data, for example by performing Maximum Power-Point Tracking
(MPPT), with the MPPT based on the power measurement factor comparison
data to provide. In the case of MPPT tracking, MPPT adjustments are made
based on the MPPT data.

[0039]Maximum Power Point Adjustments

[0040]As mentioned, the Maximum Power Point (MPP) is the point on the IV
curve which results in the maximum power. The MPP has a corresponding
current and voltage. The inverter controls the bus voltage. In a
perturb-and-observe algorithm (a reactive MPPT method), it adjusts the
voltage, measures the output and repeats.

[0041]The described techniques adapt aspects of reference and other MPPT
methods to large installations by taking advantage of the multiple
inverters typical of large installations. The described techniques are
particularly useful for large installations where the system is composed
of tens or hundreds of inverters. In the described techniques, each
inverter can operate as a "master" (reference) that uses an algorithm to
track its own MPP, or as a "slave" that periodically adjusts its
operating point to match its assigned master's. A wide variety of
embodiments are feasible, differing from each other by (1) how inverters
are selected to operate as masters or slaves, (2) which devices in the
network perform the master/slave selection, (3) what data the selection
is based on and how it is collected, and (4) how the selections are
communicated to the affected inverters.

[0042]Selection of inverters to operate as masters or slaves can be done
in several ways. Each master and its slaves can be manually selected by a
user, or automatically selected by an algorithm. Selection criteria can
include the relative physical location of the arrays in the system, the
panels' ages or test results, or MPP factor data sensed in real time.

[0043]Connecting the multiple inverters through a network enables
installation-wide mapping of MPP factors for each inverter's array at any
given time. The mapping, combined with stored data, can identify groups
of arrays that are "similarly situated" (i.e., subject to similar MPP
factors). Within these groups, the "most average" member of the group can
be identified and assigned to operate as a master until the MPP factors
change. The mapping and use of the resulting information is an aspect
that adds intelligence to the use of the existing MPPT methods. The "MPP
factor space" in which the inverters are mapped can have as many
dimensions as there are measured and stored MPP factors available: for
instance, a very simple space could have one dimension, such as physical
location or measured irradiance, or a complex MPP factor space could
include many measured and stored factors. In MPP factor space,
near-neighbor inverters are identified and their "distance" from each
other in "MPP-factor space" compared to a pre-determined error margin.
For example, if several inverters form a cluster in MPP-factor space, the
inverter closest to the center may be selected as master, and the others
within the error margin of the master may be selected as its slaves.

[0044]FIG. 5A is a diagram of an example of a simple two-dimensional MPP
space with inverters mapped and masters and slaves assigned. Other MPP
factors can also become dimensions in MPP-factor space. Such other MPP
factors can include factors sensed in real time, or factors retrieved or
calculated from stored data. Retrieved and calculated factors include
panel batch characteristics, panel age, panel location, solar zenith and
solar azimuth, all of which can become dimensions in MPP-factor space.

[0045]Depicted in FIG. 5A are a plurality of inverters, represented as
531, 532, 533, 534 and 535. By defining and plotting predetermined error
margins around each inverter, e.g., 541, 542 around inverters 531, 532, a
model consistent with electrical performance of these network components
can be generated and used. Alternatively, referring to the relative
positions of the inverters 531-535 on the graphs of FIG. 5, inverters
531-535 can be described in terms of space as defined by the axes of the
graph. This space defined by the axes of the graph can be interpreted as
MPP factor space. It is noted, that, while the error margins are shown
here as dotted circles for simplicity, they may have any suitable shape.
Typically, the error margins will often not be rotationally symmetric
because each axis is a different MPP factor measured in different units,
and the positive and negative error-margin widths may also be unequal.

[0046]Inverters 533, 534, and 535 are controlled as slaves to inverter
532, which is the most centrally located of the group. Inverter 531 is an
outlier, beyond the error margin of any of the inverters 532, 533, 534,
and 535. Therefore it is assigned to operate independently of the other
inverters as a master. Because no other inverters are within 531's error
margin 541, inverter 531 is not assigned any slaves.

[0047]The algorithm may provide contingencies for scenarios where
master/slave assignments might be unclear, as for example: [0048]1.
When a cluster spans more than one allowable error margin, grouping the
inverters in a cluster so that the smallest number of inverters are
masters and no slave follows another slave. [0049]2. When a cluster spans
more than one allowable error margin, reducing the error margin to more
clearly isolate the inverter clusters. [0050]3. Deciding which inverter
is the master if none are clearly closest to the center of a group.
[0051]4. Periodically allowing each inverter to determine its own MPP to
determine if significant shading exists. When shade is affecting one
array, its MPP may be significantly different than another array even
when all other MPP factors are identical. In this manner, the shade
factor can be determined and added as a dimension in MPP factor space
(shade may be affecting some arrays nearly equally, but significantly
different than others).

[0052]FIG. 5B is a diagram of an example of a simple two-dimensional
MPP-factor space, with inverters 552, 553, 554, 555, 558 and 559 mapped
and error margins plotted, illustrating these contingencies. Assume that
inverter 553 functions as a master for inverters 552, 554, 555 and 558.
Inverter 559 is close enough to inverter 558 to be its slave. A
contingency measure may be included in the algorithm to prevent this.
Preventing 559 from slaving to 558 is advantageous because inverter 558
is already a slave to inverter 553. Therefore, slaving 559 to 558 would
effectively slave 559 to 553, which would be inappropriate because 559
and 553 are outside each other's error margins. A similar contingency
measure can be included in the algorithm to choose which inverter
operates as the master when none of them are uniquely centered in a
cluster--for example, when only two inverters lie within each other's
error margin, both are equidistant from the center, but only one should
be a master.

[0053]FIG. 5B also illustrates how an optional feature of the algorithm
can examine alternative master/slave groupings to choose the arrangement
that minimizes the number of masters, which may be one way to maximize
plant-wide efficiency and power stability. For instance, in Grouping 1
(shown by the circles with shorter dashes), 553 could be assigned as
master of 552, 554, 555, and 558 (which lie within 553's error margin);
that would leave 559 (outside 553's error margin) as a master with no
slaves, therefore Grouping 1 would have 2 masters. Alternatively, in
Grouping 2 (shown by the circles with longer dashes), inverter 558 could
be assigned as master of 552, 553, and 559 (which lie within 558's error
margin). However, 554 and 555 are not within 558's error margin, nor are
they within each other's, so Grouping 2 would require that they both
operate as masters, resulting in a total of 3 masters for Grouping 2.
Therefore, an algorithm capable of analyzing the choices and choosing the
most advantageous under predetermined criteria can improve overall plant
performance.

[0054]The selection of masters and slaves may be implemented anywhere on
the network. A central control unit (CCU), such as a supervisory control
and data acquisition (SCADA) system can select masters and slaves
("centrally controlled" embodiments). SCADA is only given as an example
of central control, and it is possible to use different central control
schemes and other non-central control schemes. Alternatively, processing
components integrated in the inverters themselves may determine whether
each inverter operates as a master or slave, independently of whether the
network includes a CCU ("self-directed" embodiments).

[0055]The data to be applied to the networked-MPPT algorithms, which
determine the selection of masters and slaves, can be collected in
several ways. In a centrally controlled embodiment, a CCU may read MPP
factors from inverters (e.g. present operating point & master/slave
status), from array sensors (e.g., irradiance, temperature) or from
storage (e.g. age, physical location). Some centrally-controlled or
self-directed embodiments may use a CCU to collect the data and
rebroadcast it to receiving components in the inverters; this is
especially useful where several separate inverter networks communicate
with the same CCU. In other self-directed embodiments, where all the
inverters in a group of interest are connected by the same network,
receiving components in the inverters may read the data sent to the CCU
by other inverters and array sensors, and use it to make their own
determination on whether to operate as masters or slaves. In still other
self-directed embodiments, where all the inverters in a group of interest
are connected by the same network, receiving components in the inverters
may read MPP factor data broadcast by other inverters and array sensors
onto the network whether or not the network includes a CCU.

[0056]Unlike prior-art reference MPPT systems, the assignment of masters
and slaves need not be permanent. Master/slave assignments can be
re-evaluated at regular intervals and changed if a change would be
advantageous (result in higher energy capture). Alternatively, the
re-evaluation may be event-driven: that is, a change in power output or
sensed MPP factors may trigger a re-evaluation.

[0057]When a master/slave determination is made or changed for a given
inverter, the implementation of that decision can be done in several
ways. In a centrally-controlled embodiment, the CCU can issue a command
to each inverter. In self-directed embodiments, processing components in
the inverter can issue and carry out the commands, based on data
collected through the CCU or directly from other inverters and array
sensors in a "peer-to-peer" arrangement.

[0058]"Peer-to-peer" in this sense refers to how master/slave status
and/or MPP factors can be communicated between inverters. The specific
technique for communicating MPP and MPP factors depends on whether
peer-to-peer or central control is used. Variations include: [0059]1.
Inverters make the master/slave decision (self-directed embodiment) and
communicate their status and MPP factors to other inverter in a
peer-to-peer manner. No CCU is needed; however, a CCU can optionally be
used in this arrangement. [0060]2. The CCU makes the master/slave
decision in a centrally-controlled embodiment, but the actual control of
MPP is effected by the designated masters. In this arrangement, the
master inverters send their updated MPP to their slaves in a peer-to-peer
manner.

[0061]Inverters operating as masters may perform MPPT by any suitable
algorithm, including variations on predictive and reactive MPPT methods.
Once a master has found its new MPP, its assigned slaves set their
operating points to match the master's. To minimize fluctuations in power
delivered to the grid, additional algorithms resident in either a CCU or
on the individual inverters may provide for sequences and delays to
ensure that only one, or a few, different master inverters are hunting
for a new MPP (which involves varying the inverter's power output) at any
given time and to stagger slave operating point changes.

[0062]FIGS. 6A-F are diagrams showing the control operation for sets of
panels. FIG. 6A is a diagram of a representative building block for a
large scale PV system. FIG. 6B is a diagram of a large PV system typical
of conventional installations where MPPT functions are performed at each
inverter. FIG. 6c is a diagram of a representative building blocks for a
large scale networked PV system. FIG. 6D is a diagram of a large PV
system with the addition of a data network that connects each inverter to
each other and indicating that some inverters perform their own MPPT
function while other inverters operate at the MPP point of another
inverter, and are assigned by any node on the network or by each inverter
individually. FIG. 6E is a diagram of a large PV system with the addition
of a CCU (central control unit) added to the data network, in which the
CCU can make master/slave associations based on data from inverters. FIG.
6F is a diagram of a large PV system where the CCU is making MPP
decisions for each inverter.

[0063]FIG. 6A is a simplified "building block" illustrating the basis for
the more complex system diagrams to follow. The solid lines are power
channels, and the dotted lines are communication channels. Power station
601 includes PV array 611, DC power channel 612, optional sensor 614,
sensor data channel 615, and inverter 620. Inverter 620 includes the
inverter circuit module, which is identified as inverter switching
function 621, and a control module, identified as inverter MPPT function
623. The inverter circuit module 621 provides an inverter output through
power output channel 627 to substation 631. PV array 611 delivers DC
power through array power output channel 612 to inverter switching
function 621. Inverter 621 converts the array's DC power to AC power,
which it delivers through inverter power output channel 627 to power
substation 631. Optionally, a sensor system 614 may measure external MPP
factors affecting array 611 and send them through sensor data channel 615
to inverter MPPT function 623 (represented by a symbolic I-V curve).
Array 611, inverter switching function 621, inverter MPPT function 623,
optional sensor 614, and channels 612, 615 that connect them together
comprise power station 601.

[0064]FIG. 6B demonstrates the prior art, in which each inverter performs
MPPT independently for its own array; that is, every inverter acts as a
master.

[0065]FIG. 6c is a simplified "building block" illustrating the basis for
the more complex system diagrams to follow. The solid lines are power
channels, and the dotted lines are communication channels. Power stations
641, 642 each include PV array 611, DC power channel 612, optional sensor
614, sensor data channel 615, and inverter 645. Inverter 645 includes the
inverter circuit module, which is identified as inverter switching
function 621, and a control module, identified as inverter MPPT function
646. Inverter 645 provides an inverter output through power output
channel 627 to substation 631. Additionally, the inverter control module
is capable of interacting with other inverter control modules in
different inverters 645 to permit control as a master (in power stations
641) or slave (in power stations 642). PV array 611 delivers DC power
through array power output channel 612 to inverter switching function
621. Inverter switching function 621 converts the array's DC power to AC
power, which it delivers through inverter power output channel 627 to
power substation 631. Optionally, a sensor system 614 may measure
external MPP factors affecting array 611 and send them through sensor
data channel 615 to inverter MPPT function 646. Array 611, inverter
switching function 621, inverter MPPT function 646, optional sensor 614,
and channels 612 and 615 that connect them together comprise power
stations 641, 642.

[0066]The inverter can function a master, depicted as inverter 645 in
power station 641, or a slave, depicted as inverter 645 in power station
642. Inverter 645 in power station 641 functioning as a master performs
MPPT functions for that array, as determined by control module 646.
Inverter 645 in power station 642 functioning as a slave performs power
point adjustments for that array as determined externally by a master
(e.g., by control module 646 of inverter 645 in power station 641).
Therefore, if the control module is in a slave mode, as represented at
control module 646a in power station 642, that control module 646a is
responsive to an external control module. It is possible for a slave
control module to have the capability to function as a master when no
other suitable master is available.

[0067]FIG. 6D shows the general case, in which inverter MPPT functions
interact (sending, receiving, or both) with generalized inverter
communication network 649. Power stations 641 and 642 on the data network
have a master/slave assignment. Many variations on the nature of inverter
communication network 649, and on the information sent or received by
inverter MPPT function 646, are possible. Each of the following example
approaches to networked MPPT (NMPPT) technique uses the networking of
inverters to assign some of the inverters to operate as slaves to
appropriate masters. Some may use the network to map the inverters in
MPP-factor space, and use the map to choose the optimal number of
masters. Several variations on this theme could be implemented:

[0068]Centrally Controlled NMPPT: A central control unit (CCU) 661, which
may be, by way of non-limiting example, a supervisory control and data
acquisition (SCADA) system, is included. The CCU is part of the network
and performs some of the functions. FIG. 6E illustrates a centrally
controlled embodiment. The network can operate in either of the following
ways: [0069]a. Inverters send their measurements to the CCU, where
other, non-measured MPP factors reside. The CCU periodically recalculates
the MPP-factor map, assigns inverters to master or slave status, and
sends a command to each slave inverter with its new commanded operating
point. [0070]b. Same as a. above (inverters send their measurements to
the CCU), except the inverters can read each other's operating point
through the network, so slaves can follow their masters through multiple
MPPT cycles until the CCU assigns them to different masters or commands
them to become masters.

[0071]In centrally-controlled embodiments where a CCU is present, the CCU
itself may perform maximum power point tracking for specific inverters or
for the system as a whole and communicate commanded operating points to
each inverter. In this example, all inverters are slaves of the CCU.

[0072]FIG. 6F shows an alternate embodiment where CCU 671 includes an
internal MPPT function and the inverters are, in effect, slaved to the
CCU. The MPPT resides in CCU 671 (represented by a symbolic I-V curve),
and all inverters "slave" to CCU 671, based on the internal MPPT
function. The CCU may determine, as shown here, a single MPP for all the
inverters; alternatively, it may determine a set of MPPs for each
inverter separately or for multiple subgroups of inverters.

[0073]Self-Directed NMPPT: Inverters analyze their own MPP factors and
those of other inverters to make their own master/slave decisions.
[0074]a. Inverters send their measurements to the CCU, but the CCU only
rebroadcasts the measurements to all the inverters, appending any
non-measured MPP factors.

[0075]Each inverter analyzes the positions of its neighbors in MPP factor
space and makes its own decision whether to function as a master or a
slave. The CCU rebroadcast is useful when not all the inverters are on
the same network. [0076]b. Inverters connected as in FIG. 6E send their
measurements and non-measured MPP factors to the CCU while reading what
all (or some) of the other inverters are sending. Inverters make their
own master/slave decisions based on their readings. The CCU only monitors
the data for use in performance evaluation or sending maintenance alerts.
[0077]c. The CCU does not participate in the NMPPT process and need not
even be part of the network (as in a literal, rather than symbolic,
interpretation of the network in FIG. 6D). Inverters broadcast their
measurements and non-measured MPP factors to each other and make their
own master/slave decisions.

[0078]Control Configurations

[0079]FIGS. 7 and 8 are example control configurations for a plurality of
arrays.

[0080]FIG. 7 is a flowchart of an example algorithm for a
centrally-controlled configuration, showing the process that can take
place in the CCU. This configuration is an example of an algorithm in
which a CCU maps MPP-factor space and commands slaves to set their
operating points based on MPP data read from assigned masters.
Appropriate master/slave assignments are sorted by the algorithm to
ensure that masters and slaves are of similar age and are similarly
situated geographically, as well as having similar real-time measured MPP
factors (MPP factor space mapping).

[0081]Information 711-713 regarding MPP factors is obtained. The
information includes information 711 in the form of a lookup table
regarding acceptable geographic master/slave groupings, information 712
in the form of a lookup table regarding acceptable panel-age-based
master/slave groupings, real-time measured MPP factors 713 for all
inverters, including information from sensors. The sensors may be optical
sensors, thermal sensors, and other types of sensors. The information is
used to map MPP-factor space, assign masters & slaves (step 721).
Commands are then issued (step 722) to the slaves and masters, in which
the slaves stop MPPT and the masters begin or resume MPPT. New MPP
operating points are gathered from the masters (step 723). The operating
points from the masters are then sent to the slaves (step 724) and the
slaves operated at those operating points. In any of these
configurations, any suitable MPPT method may be used by the assigned
master inverters or the CCU, including the presently known methods of
predictive, reactive and reference MPPT.

[0082]The process is repeated at fixed intervals (step 731).

[0083]FIG. 8 is a flowchart of an example algorithm for a self-directed
configuration, showing a process that can take place within each
inverter. In any of these configurations, any suitable MPPT method may be
used by the assigned master inverters or the CCU, including the presently
known methods of predictive, reactive and reference MPPT.

[0084]Information 811-814 regarding MPP factors is obtained. The
information includes information 811 in the form of a lookup table
regarding geographical neighbor inverters, information 812 in the form of
a lookup table regarding panel age of neighbor inverters, the subject
inverter's real-time MPP factors 813 and other inverters' real-time
factors 814. The information is used to compare (step 821) the subject
inverter's MPP factors with MPP factors of its neighbors. The particular
neighbor having MPP factors closest to those of the subject inverter is
identified (step 822) and a determination (step 823) is made of whether
the MPP factors of the closest neighbor is within an error margin.

[0085]In the case of the MPP factors of the closest neighbor is within the
error margin, the neighbor is made the temporary preferred master (TPM,
step 824) and a determination (step 831) is made as to whether a TPM was
found. In the case of MPP factors of the closest neighbor not being
within the error margin, or in the case of there being no TPM, the
subject inverter is set as the master and a master flag is set to
"Master" (step 832).

[0086]If a TPM exists (determination step 831), a determination is made
whether the inverter is already a master (existing status flag is already
set to "Master", step 841), and if not, a determination (step 843) is
made whether the subject inverter is slaved to the TPM. If the subject
inverter is not slaved to the TPM, the status flag of the subject
inverter is set to "Slave" and the master is the TPM. In either case,
meaning the subject inverter is slaved to TPM (determination 843) or the
status flag is already set to "Slave" (step 841,3), the result is the
same, meaning the subject inverter is slaved to the TPM and the status
flag set to "Slave". A match is made (step 851) of the TPM's operating
point, and a self-reference check (to prevent multiple inverters from
slaving to each other) is performed (step 852). A determination (step
853) is made of whether the self reference check passed.

[0087]In the case of the self reference check not passing (determination
853) a determination (step 855) is made of whether the subject inverter's
ID is greater than the TPM ID. If the subject inverter's ID is greater
than the TPM ID (determination 855), then the subject inverter is slaved
to the TPM until the next repeat of the sequence. The determination of
whether the subject inverter's ID is greater than the TPM ID is a very
basic self reference algorithm. This sequence is given as an example of a
self-reference algorithm, and is not intended to exclude other
techniques.

[0088]If the subject inverter's ID is not greater than the TPM ID
(determination 855), then the status flag is set to master (step 832).

[0089]In the case of the status flag already being set to "Master" as
determined at determination 841, a determination (step 871) is made of
whether there are any current slaves, and if not, the inverter is allowed
to slave to the TPM (step 845). If the determination (at step 871) is
that there are current slaves, MPPT is performed (step 873) and the
subject inverter is operated as a Master until the next repeat of the
sequence.

[0090]MPPT is also performed (step 873) in response to setting of the
master flag to "Master" (step 832).

[0091]The process repeats at fixed intervals (step 881) by returning to
the comparing of the subject inverter's MPP factors with MPP factors of
its neighbors at step 821.

[0092]These possible, but not essential, enhancements can work with
several variations, non-limiting examples being:

[0093]Predictive operation: Extra storage and analysis capability is added
to either the CCU or the inverters so they can use MPP-factor map history
to predict what will happen next, reducing lag time between MPP changes
and inverter voltage corrections. For instance, a moving cloud will cause
a traveling ripple in irradiance across adjacent arrays. The speed and
direction of the ripple can be measured, and the next arrays in the path
will adjust for it as (instead of after) it reaches them.

[0094]Predictive MPPT approaches set the operating point of the PV array
based on a predetermined constant value or based on an algorithm that
adjusts the operating point based on inputs such as time of day, actual
or predicted irradiance levels, or actual or predicted cell temperature.
One predictive MPPT approach is the "optimized fixed voltage" method,
where each panel or array is operated at the fixed operating point that
will stay nearest the MPP over the course of an "average day"; the fixed
operating point can be determined by models or sets of previous
measurements. Another predictive MPPT approach is voltage scheduling,
where a timer changes the array operating point by increments based on
expected MPP changes as time goes by. Advanced voltage-scheduling
algorithms can account for cell age as well as expected daily and
seasonal irradiance and temperature changes.

[0095]Peer-to-peer communication: While master/slave relationships last,
masters communicate their MPP changes and MPP factors directly to all
their slaves, speeding up responses and simplifying processing. Slaves
periodically monitor the general MPP-factor traffic and decide whether to
become slaves to another master or become masters themselves.

[0096]Manual overrides: [0097]a. An operator can assign some inverters
to always be masters, and the rest to be slaves to whichever master is
closest in MPP space. [0098]b. An operator can prevent inverters from
becoming slaves to certain other inverters (for instance, those
geographically too far away, or pointing at a different angle, or still
being "burned in" after installation.). A "No Follow Flag" may be used in
order to keep other inverters from slaving to an inappropriate master.
[0099]c. An operator can assign a sequence or delay between masters
performing MPPT, so that only one, or a few, masters are doing so at any
given time. This confines the power fluctuations associated with the MPPT
process to a small fraction of the total power produces by the
installation at any given time.

[0100]Automatic Override by Inverter: It is possible to permit the
inverter to override the CCU in instances where a MPP factor exceeds a
predetermined threshold. In centrally controlled embodiments, where a
master is performing MPPT on its respective array, but the polling
frequency from the CCU is such that the inverter may perform several MPPT
operations between CCU polls, the inverter can initiate a message to the
CCU if a deadband threshold is exceeded between MPPT operations. In such
instances, the CCU could initiate recalculation of MPP factor space. By
way of example, if the deadband threshold is exceeded from one MPPT
operation to another to initiate recalculation of MPP factor space and/or
immediately direct slaves to begin operating as their own masters.

[0101]Software Implementation

[0102]The operation and control features can be implemented in hardware,
software or a combination of hardware and software. In the case of
software, the software may be embodied in storage media or as firmware.
Storage media and computer readable media for containing code, or
portions of code, can include any appropriate media known or used in the
art, including storage media and communication media, such as but not
limited to volatile and non-volatile, removable and non-removable media
implemented in any method or technology for storage and/or transmission
of information such as computer readable instructions, data structures,
program modules, or other data, including RAM, ROM, EEPROM, flash memory
or other memory technology, CD-ROM, digital versatile disk (DVD) or other
optical storage, magnetic cassettes, magnetic tape, magnetic disk storage
or other magnetic storage devices, data signals, data transmissions, or
any other medium which can be used to store or transmit the desired
information and which can be accessed by the computer. Based on the
disclosure and teachings provided herein, a person of ordinary skill in
the art will appreciate other ways and/or methods to implement the
various embodiments.

[0103]Conclusion

[0104]It will be understood that many additional changes in the details,
materials, steps and arrangement of parts, which have been herein
described and illustrated to explain the nature of the described
technique, may be made by those skilled in the art within the principal
and scope of the invention as expressed in the appended claims.